Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability
Satellite-derived vegetation phenophases are frequently used to study the response of ecosystems to climate change. However, limited studies have identified the common phenological variability across different climate and vegetation zones. Using NOAA/Advanced Very High Resolution Radiometer (AVHRR)...
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doaj-a8211b3d5d354ead9934e6dbc606fd832020-11-24T22:40:53ZengMDPI AGRemote Sensing2072-42922016-05-018543310.3390/rs8050433rs8050433Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate VariabilityQuansheng Ge0Junhu Dai1Huijuan Cui2Huanjiong Wang3Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, ChinaSatellite-derived vegetation phenophases are frequently used to study the response of ecosystems to climate change. However, limited studies have identified the common phenological variability across different climate and vegetation zones. Using NOAA/Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset, we estimated start of growing season (SOS) and end of growing season (EOS) for Chinese vegetation during the period 1982–2012 based on the Midpoint method. Subsequently, the empirical orthogonal function (EOF) analysis was applied to extract the main patterns of phenophases and their annual variability. The impact of climate parameters such as temperature and precipitation on phenophases was investigated using canonical correlation analysis (CCA). The first EOF mode of phenophases exhibited widespread earlier or later SOS and EOS signals for almost the whole country. The attendant time coefficients revealed an earlier SOS between 1996 and 2008, but a later SOS in 1982–1995 and 2009–2012. Regarding EOS, it was clearly happening later in recent years, mainly after 1993. The preseason temperature contributed to such spatiotemporal phenological change significantly. The first pair of CCA patterns for phenology and preseason temperature was found to be similar and its time coefficients were highly correlated to each other (correlation coefficient >0.7). These results indicate that there is a substantial amount of common variance in SOS and EOS across different vegetation types that is related to large-scale modes of climate variability.http://www.mdpi.com/2072-4292/8/5/433remote sensing phenologygrowing seasonNDVIcanonical correlation analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Quansheng Ge Junhu Dai Huijuan Cui Huanjiong Wang |
spellingShingle |
Quansheng Ge Junhu Dai Huijuan Cui Huanjiong Wang Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability Remote Sensing remote sensing phenology growing season NDVI canonical correlation analysis |
author_facet |
Quansheng Ge Junhu Dai Huijuan Cui Huanjiong Wang |
author_sort |
Quansheng Ge |
title |
Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability |
title_short |
Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability |
title_full |
Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability |
title_fullStr |
Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability |
title_full_unstemmed |
Spatiotemporal Variability in Start and End of Growing Season in China Related to Climate Variability |
title_sort |
spatiotemporal variability in start and end of growing season in china related to climate variability |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2016-05-01 |
description |
Satellite-derived vegetation phenophases are frequently used to study the response of ecosystems to climate change. However, limited studies have identified the common phenological variability across different climate and vegetation zones. Using NOAA/Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset, we estimated start of growing season (SOS) and end of growing season (EOS) for Chinese vegetation during the period 1982–2012 based on the Midpoint method. Subsequently, the empirical orthogonal function (EOF) analysis was applied to extract the main patterns of phenophases and their annual variability. The impact of climate parameters such as temperature and precipitation on phenophases was investigated using canonical correlation analysis (CCA). The first EOF mode of phenophases exhibited widespread earlier or later SOS and EOS signals for almost the whole country. The attendant time coefficients revealed an earlier SOS between 1996 and 2008, but a later SOS in 1982–1995 and 2009–2012. Regarding EOS, it was clearly happening later in recent years, mainly after 1993. The preseason temperature contributed to such spatiotemporal phenological change significantly. The first pair of CCA patterns for phenology and preseason temperature was found to be similar and its time coefficients were highly correlated to each other (correlation coefficient >0.7). These results indicate that there is a substantial amount of common variance in SOS and EOS across different vegetation types that is related to large-scale modes of climate variability. |
topic |
remote sensing phenology growing season NDVI canonical correlation analysis |
url |
http://www.mdpi.com/2072-4292/8/5/433 |
work_keys_str_mv |
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